OPTIMIS PRO – A Novel Algorithm to Improve Perioperative Antibiotic Administration
Bibliographic record
Abstract
Objectives The selection and dosing of surgical antimicrobial prophylaxis (SAP) to prevent surgical site infections (SSIs) is often improvisational and inappropriate in clinical settings resulting in increased risk of SSI. We therefore developed and implemented a novel computer decision support tool, OPTIMIS PRO (OPTIMIzing PROphylaxis), to improve appropriate SAP selection specific to each patient’s procedure and clinical context. Methods: This quality improvement study was conducted at a tertiary hospital network over 2 years, divided into pre-intervention (June 2021-June 2022) and post-intervention (June 2022-June 2023) periods. The intervention was a computer decision support tool programmed within the hospital’s health information system to provide patient-specific SAP recommendations based on four variables: procedure name, patient’s beta-lactam allergy status, MRSA status, and weight. Approximately 3046 unique surgical procedures were identified and a specific best practice SAP recommendation for each surgery was identified based on international practice guidelines, up-to-date literature, and panel expertise input from 14 surgical divisions at our institution. Safety of cefazolin prophylaxis among patients with self-reported beta-lactam allergy was established in the pre-operative clinic using a validated simple two-item questionnaire (Figure 1). During each standard preoperative preparation, a best practice SAP recommendation alert was then provided to the responsible anesthesiologist based on the inputs from the four aforementioned variables (Figure 2). To assess the impact of the OPTIMIS PRO tool on antibiotic prescribing, we retrospectively audited SAP selection before and after implementation, also assessing appropriateness for each of the specific inputs using evidence-based criteria. Results: Over 30 000 OPTIMIS PRO recommendation alerts were logged in the 12-month post-intervention period. A random sample audit of 408 surgical encounters were selected from the pre- and post-intervention period for analysis. Overall, appropriate antibiotic administration rose from 77% (161/208) to 92.5% (185/200) (x2=18.0, p < 0 .001) post-intervention. Usage of cefazolin among patients reporting a beta lactam allergy rose from 48% (16/33) to 100% (12/12). None of these 12 patients experienced adverse reactions as a result of beta lactam exposure. Appropriate antibiotic selection based on MRSA status was high pre- and post-implementation (98.4% vs 99.4%); but significant improvements were made for procedure-specific antibiotic selection (80.5 vs 94.5%; x2=19.3, p < 0 .001) and weight-based dosing (92.5% vs 98.4%; x2=7.45, p=0.006). Conclusion: In this first-ever intervention designed to direct SAP prescribing based on patient specific variables, we significantly improved appropriate SAP selection across a comprehensive list of surgical procedures. Future analysis should include assessing potential reductions in SSIs as result of using the support
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".